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Information Propagation and Encoding in Solids: A Quantit...
[Submitted on 28 Jan 2026 (v1), last revised 31 Jul 2026 (this v · 2026-01-29 · via math updates on arXiv.org

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Abstract:Engineered systems typically separate mechanical function from information processing, whereas biological systems can exploit physical structure as a medium for information processing and computation. Motivated by this contrast, recent work in mechanics has explored embedding information-processing capabilities directly into mechanical structures. However, quantitative frameworks for evaluating such capabilities remain limited. Here we address a foundational question: how does information propagate through a solid body? Using elastic bodies as a model system, we apply information-theoretic tools to treat an elastic domain as an information encoder and quantify how information transmits from applied loads to discrete sensor locations. We further connect these measures to familiar mechanical phenomena, including Saint-Venant's effect and principal stress lines. Moving toward design, we show how geometry and architected materials can tune transmission, enabling elastic domains to either transmit or block information. Overall, this work advances quantifiable metrics and benchmark tasks for mechanical intelligence, supporting comparable designs of mechanically embodied information processing.

Submission history

From: Peerasait Prachaseree [view email]
[v1] Wed, 28 Jan 2026 22:40:52 UTC (7,426 KB)
[v2] Tue, 16 Jun 2026 23:41:53 UTC (10,721 KB)
[v3] Fri, 31 Jul 2026 05:13:24 UTC (11,209 KB)